Accurate characterization of aircraft turbulence is vital for aviation safety and efficiency. This study leverages 2021 data from nationwide Pilot Reports (PIREPs) and China Eastern Airlines’ in situ Eddy Dissipation Rate (EDR) measurements to systematically compare these two primary turbulence monitoring sources. We quantify their consistencies and discrepancies in capturing turbulence intensity and spatiotemporal patterns to assess their respective value and limitations. The findings indicate that while the diurnal and monthly variation trends of turbulence distributions are generally consistent between the two datasets, significant differences exist in intensity distribution, vertical profiles, and spatial patterns. By examining 242 turbulence events concurrently recorded by both China Eastern Airlines’ EDR and pilot reports, the study identifies a spatial discrepancy within 40 km and an average reporting delay of approximately two minutes in PIREPs, with the delay becoming more pronounced as turbulence intensity increases. Furthermore, pilot-reported “severe” turbulence corresponds to EDR values notably lower than the ICAO standard, revealing a systematic overestimation bias.

A comparison was made of two eddy dissipation rate (EDR) estimates based on flight data recorded by commercial flights. The EDR estimates from real-time data using the National Center for Atmospheric Research (NCAR) Algorithm were compared with the EDR estimates derived using the Netherlands Aerospace Centre (NLR) Algorithm using quick assess recorder (QAR) data. The estimates were found to be in good agreement in general, although subtle differences were found. The agreement between the two algorithms was better when the flight was above 10,000 ft. The EDR estimates from the two algorithms were also compared with the vertical acceleration experienced by the aircraft. Both EDR estimates showed good correlation with the vertical acceleration and would effectively capture the turbulence subjectively experienced by pilots.

Convectively induced turbulence (CIT) is a severe aviation hazard. It is challenging to forecast CIT because low-resolution models cannot explicitly resolve convective motions at kilometer scales. In this study, we used the Model for Prediction Across Scales (MPAS) to simulate CIT cases with convection-permitting resolution ( $$\sim $$ ∼ 1 km) in the region of the CIT events and coarse resolution in other parts of the globe. We developed a method to estimate the eddy dissipation rate (EDR) using the resolved wind field of the MPAS simulations. The method is based on explicit filtering and reconstruction in the turbulence modeling for large-eddy simulations (LES). It estimates turbulence kinetic energy (TKE), which is then used to derive EDR. The new method produces different turbulence distribution and intensity than previous methods based on second-order structure functions and convective gravity wave drag, with higher accuracy and better correlation with observations for CIT cases tested in this study. The 1-km resolution simulation generates more accurate EDR and improves spatial patterns, but it is computationally demanding. The 3-km resolution can get benefits from reasonable accuracy and affordable computational cost. Because convection-permitting resolutions are in the gray zone for simulating convection, we evaluated the sensitivity of the prediction to the variations in physical and numerical schemes. Varying cumulus convection parameterization and monotonicity of numerical schemes are identified as practical approaches to generate beneficial ensemble spread. However, the physical perturbation-based ensemble has limitations, and initial condition perturbations are still necessary to encompass uncertainties in the development of convection.

Cloud liquid water content (CLWC) based on microwave radiometer data was investigated in this study. First, its consistency with radiosonde-based CLWC was established. Integrated CLWC was also checked against the liquid water path. CLWC performance in four weather types was considered: dense fog, clouds in spring, rainstorms, and typhoons. CLWC provides new insights into weather events. In particular, it could be useful for nowcasting low visibility associated with sea fog. It was also found to be inversely proportional to visibility in two cases of low visibility in Hong Kong. In springtime, low-level clouds and liquid water were found to exist extensively inside clouds. In rainstorm cases, supercooled cloud liquid water was absent during heavy rain but may exist within clouds when rain stops or light rain occurs. Similar observations were made in typhoon cases, namely during the direct impact of Typhoon Wipha on Hong Kong. Supercooled cloud liquid was present when outer rainbands of the typhoon affected Hong Kong with a smaller amount of rainfall. However, when Hong Kong was hit by a typhoon’s eyewall, rain was heavier, and supercooled liquid water was absent. These features are consistent with the radiosonde-based CLWC profiles. Radiometer-based CLWC is pseudocontinuous and provides additional insight into liquid water distribution in clouds under various weather conditions.